Software Development Engineer

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer to build and operate ML infrastructure, data pipelines, and low-latency ad serving systems for personalized ad experiences on Amazon.com. This role involves developing ML models, integrating them into large-scale distributed systems, and using A/B experimentation to improve relevance and performance.

What you'd actually do

  1. Design, build, and operate ML infrastructure and data processing pipelines that power ad relevance and sourcing at massive scale
  2. Develop and optimize machine learning models incorporating deep product and shopper understanding to identify relevant advertisements across non-Search surfaces
  3. Architect and build ad serving systems that solve real-world customer use cases with high volume, low latency, and strict availability requirements
  4. Integrate ML solutions with large-scale distributed systems for click-through prediction and ad auction
  5. Measure impact and customer response through rapid A/B experimentation, iterating to improve relevance and performance

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • massive scale
  • high volume, low latency, and strict availability requirements

Other signals

  • ML models
  • large-scale data pipelines
  • low-latency ad serving systems
  • A/B experimentation
  • Amazon scale